1. Field
One or more aspects of embodiments according to the present invention relate to automatic target recognition and more particularly to combining several classifiers in a system and method for automatic target recognition in radar images.
2. Description of Related Art
Inverse synthetic aperture radar (ISAR) is a signal processing technique used to form a two-dimensional (2-D) radar image from moving target objects by separating radar returns in Doppler frequency and in range. ISAR is possible with or without radar platform motion. An ISAR 2-D image is comprised of different intensity pixels of reflected point scatterers located at particular range and Doppler bin indices. Different Doppler shifts arise from different points along the rotating target, each point having its own line of sight (LOS) velocity toward the radar. Currently existing ISAR Automatic Target Recognition (ATR) systems may rely on a human component; in such systems trained operators look at ISAR images and match certain target features to canned templates. Such features may include the apparent length of a target and its proportions to dominant scatterer locations. Existing ATR systems are primarily based on look-up tables, which utilize user-defined features and a classifier to realize the closest template match to a given target. Existing ATR systems may be developed using simulated data, which may lack important characteristics of real data, such as noise spikes, competing land and high sea state clutter returns, range and Doppler smearing, and atmospheric confrontations; such systems may be prone to errors, and may become confused when presented with targets that do not correspond to a known target type. Moreover, an existing ATR system may not provide an assessment of the confidence with which an identification is made. Thus, there is a need for an ATR system with improved reliability, which generates confidence estimates, and which can accommodate unknown target types.